Lung cancer and imaging /

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"Version: 20191201"--Title page verso.Includes bibliographical references.1. Early diagnosis system for lung nodules based on the integration of a higher-order MGRF appearance feature model and 3D-CNN -- 1.1. Introduction -- 1.2. Methods -- 1.3. Experimental results -- 1.4. Conclusion2. Capsule networks for lung cancer screening -- 2.1. Introduction -- 2.2. Capsule network -- 2.3. Fast capsule network -- 2.4. Dataset -- 2.5. Experiments -- 2.6. Results and discussion -- 2.7. Conclusions3. Quantitative malignancy recognition of lung cancer using non-invasive image modalities -- 3.1. Introduction -- 3.2. Materials and methods -- 3.3. Conclusion4. Epidemiology of lung cancer -- 4.1. Descriptive epidemiology of lung cancer -- 4.2. Risk factors of lung cancer -- 4.3. Lung cancer in never-smokers -- 4.4. Screening -- 4.5. Conclusion5. Use of biomarkers in lung cancer diagnosis, prognosis, and treatment -- 5.1. Introduction -- 5.2. Histological subtypes and respective personalized medicine -- 5.3. Available screening assays to detect molecular alterations and genetic rearrangements -- 5.4. Molecular methods used to detect mutations -- 5.5. Genomic markers -- 5.6. Proteomic markers -- 5.7. Metabolic markers -- 5.8. Immunotherapy markers -- 5.9. The emerging role of microRNAs -- 5.10. Clinical trials with targetable oncogenic drivers -- 5.11. Conclusion6. Radiomics and lung cancer : promising news for early detection of nodules -- 6.1. Introduction -- 6.2. Interpretation of small lung nodules -- 6.3. Computer-aided detection/diagnosis (CAD) -- 6.4. Radiomics -- 6.5. Conclusion7. Photodynamic diagnosis and treatment of lung cancer -- 7.1. Introduction -- 7.2. Cancer -- 7.3. Photodynamic diagnosis -- 7.4. Photodynamic therapy -- 7.5. Conclusion8. Cold atmospheric plasma and iron oxide based magnetic nanoparticles for synergetic lung cancer therapy -- 8.1. Introduction -- 8.2. Therapeutic effect of cold atmospheric plasma in lung cancer -- 8.3. The therapeutic effect of magnetic iron oxide nanoparticles in lung cancer -- 8.4. Synergistic therapeutic effects of cold atmospheric plasma and magnetic iron oxide nanoparticles in lung cancer -- 8.5. The synergistic therapeutic effect of cold atmospheric plasma and drug-loaded magnetic nanoparticles in lung cancer -- 8.6. Conclusions9. Exploiting exhaled aerosol fingerprints to detect lung cancers and obstructive respiratory diseases -- 9.1. Introduction -- 9.2. Methods and materials -- 9.3. Results -- 9.4. Discussion -- 9.5. Conclusion10. A study of ground-glass opacity (GGO) nodules in the automated detection of lung cancer -- 10.1. Introduction -- 10.2. Ground-glass opacity (GGO) nodules -- 10.3. Computer-aided detection of GGO nodules -- 10.4. Different ways to handle GGOs in automated detection -- 10.5. Conclusion11. Electromagnetic imaging and lung ablation -- 11.1. Introduction -- 11.2. Electrical impedance tomography -- 11.3. Magnetic induction tomography -- 11.4. Microwave imaging -- 11.5. Lung ablation -- 11.6. Current trends and future perspectives -- 11.7. Conclusion.Lung cancer is one of the most common cancers in both men and women worldwide. Early diagnosis of lung cancer can significantly increase the chances of a patient's survival, yet early detection has historically been difficult. As a result, there has been a great deal of progress in the development of accurate and fast diagnostic tools in recent years. Lung Cancer and Imaging provides an introduction to both the methods currently used in lung cancer diagnosis and the promising new techniques that are emerging. Areas covered include the major trends and challenges in lung cancer detection and diagnosis, classification of cancer types, lung feature extraction in joint PET/CT images, and algorithms in the area of low dosage CT lung cancer images. Part of Series in Physics and Engineering in Medicine and Biology.Medical imaging students, researchers and practitioners.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Ayman El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, Kentucky. Dr El-Baz has 17 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. In 2009, he was named a Coulter Fellow for his contributions to the field of biomedical translational research. He has authored or co-authored more than 500 technical articles across journals, books, book chapters, conference papers, abstracts, and US patents and Disclosures. Jasjit S. Suri is an innovator, scientist, visionary, industrialist and an internationally known world leader in biomedical engineering. Dr Suri has spent over 25 years in the field of biomedical engineering/devices and its management. He received his PhD from the University of Washington, Seattle and his Business Management Sciences degree from Weatherhead, Case Western Reserve University, Cleveland, Ohio. Dr Suri was crowned with President's Gold medal in 1980 and made Fellow of the American Institute of Medical and Biological Engineering for his outstanding contributions. In 2018, he was awarded the Marquis Life Time Achievement Award for his outstanding contributions and dedication to medical imaging and its management.Title from PDF title page (viewed on January 6, 2020).

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Series Title

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Publisher

: .,

Collation

1 online resource (various pagings) :illustrations (some color).

Language

English

ISBN/ISSN

9780750325400

Classification

616.99/424075

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-

Edition

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Subject(s)

Radiography.
Diagnostic Imaging
Medical imaging.
Lungs
Lung Neoplasms
MEDICAL / Allied Health Services / Imaging Technol

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Statement of Responsibility

edited by Ayman El-Baz, Jasjit S. Suri.

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